import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def init(self):
      super(Net, self).init()
      self.conv1 = nn.Conv2d(1, 6, 5)
      self.pool = nn.Conv2d(6, 6, 5)
      # 平均池化层
      self.conv2 = nn.Conv2d(6, 16, 5)
      self.fc1 = nn.Linear(16 * 5 * 5, 120)
      self.fc2 = nn.Linear(120, 84)
      self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.avg_pool2d(x, (2, 2)) # 平均池化层
        x = F.relu(self.conv2(x))
        x = F.avg_pool2d(x, (2, 2)) # 平均池化层
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features